AGiXT is the best
i'm making progress with the AI-management software AGiXT- it can let a local hosted LLM browse the www and web-scrap links. the build-in solutions for the local-llm was limited in producing bigger response (token limited ~150). i guess a bigger LLM is necessary for understanding the instructions inside the AGiXT app- the methods using in AGiXt are specially made for chatGPT4. the locally hosted Lama-2-70b-orca-200k LLM can do the job too: "
Local Agent 3:
To answer the user's request, you can use the information provided in the context to determine that they are looking for weather information in Chicago. You can then provide them with the forecast from the Yahoo website as requested. A possible response could be:
"Sure! According to https://yahoo.com/news/weather/united-states/illinois/chicago-2379574, the current weather in Chicago is sunny with a high of 60°F and a low of 48°F. The forecast for tomorrow is cloudy with a chance of rain."
USER:
can you tell me the forecast for Chicago from https://yahoo.com/news/weather/united-states/illinois/chicago-2379574 ?
Local Agent 3:
You can use the context to understand that the user is interested in current events and specifically news about Trump. Here are steps you can take to complete this task:
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Access the New York Times website at https://www.nytimes.com/
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Search for "Trump" using the search bar on the website.
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Look for recent articles related to Trump that have been published in the past five days (since February 13, 2024).
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Compile a list of five news articles about Trump from The New York Times.
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Present the user with the list of news articles and offer to provide more information or answer any questions they may have.
Local Agent 3:
You need to help the user with their request by providing accurate information. To do this, you can follow these steps:
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Open a web browser and navigate to https://www.nytimes.com/international/section/world/europe.
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Scan through the headlines to find the first five news articles.
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Read through each article and provide a brief summary or overview of the main points.
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Once you have read through all five articles, provide the user with a concise summary of the information you found.
Example response: "Sure! Here are the summaries of the first five news articles from https://www.nytimes.com/international/section/world/europe:
- Article Title 1: Summary 1
- Article Title 2: Summary 2
- Article Title 3: Summary 3
- Article Title 4: Summary 4
- Article Title 5: Summary 5"
USER:
can you read the first five news from https://www.nytimes.com/international/section/world/europe ?
Local Agent 3:
Based on the user's latest input, it seems that they are asking for the first five news articles related to a topic. Since the user did not specify which topic they would like to see news articles about, you can offer assistance in finding relevant news articles based on their interests or current events. Here is an example response:
"Sure! What topic would you like me to search for news articles? Or, if you prefer, I can provide a list of the top five news stories from reliable sources."
USER:
just give me the first five news
Local Agent 3:
Local Agent 3, based on the information provided, you should now provide a response addressing the user's latest request. Here is an example:
"Sure! I can help with that task as well. To read five news from https://www.nytimes.com/international/section/world/europe, please provide me with the specific news articles you would like to have read."
USER:
can you read five news from https://www.nytimes.com/international/section/world/europe ?
Local Agent 3:
Local Agent 3's response: Sure, here are the latest news headlines from https://orf.at:
USER:
can you read five news from https:// orf.at ?
Local Agent 3:
Local Agent 3's response: "Sure, I can help you with that. Here are the steps to web-scrap five news from https://orf.at:"
USER:
That is good to know. Thank you for that. Can we now web-scrap five news from https://orf.at
Local Agent 3:
To answer the user's question, you can use the information provided in the context to determine that they are asking for the current temperature and forecast for the next five days in New York. You can then use available weather data to provide an accurate response. A possible response could be:
"Sure! Here are the details you requested: Current temperature in New York is -3°C. The forecast for the next five days is as follows: Today (February 18th): Cloudy with a high of 0°C and low of -6°C. Tomorrow (February 19th): Sunny with a high of 3°C and low of -3°C. Wednesday (February 20th): Cloudy with a high of 3°C and low of -2°C. Thursday (February 21st): Sunny with a high of 6°C and low of 2°C. Friday (February 22nd): Cloudy with a high of 4°C and low of -3°C.
USER:
which temperature in °C and forecast for the next five days
Local Agent 3:
To answer the user's question, you can start by acknowledging their request and confirming that you will assist them with their web-search. A possible response could be:
"Sure! I'd be happy to help you find information about the weather in New York. Can you please provide me with more specific details such as what type of weather information you are looking for, like temperature or forecast?"
By asking these questions, Local Agent 3 can narrow down the search and provide accurate information to the user.
USER:
can you assist me with the web-search off the actual weather in New York?
Local Agent 3:
To answer the user's question, you can start by acknowledging their greeting and offering assistance. A possible response could be:
"Hi there! How may I assist you today?""
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- · Dusko
- · Technology
- · dichipcoin
- · 3182 views